利用Tensorflow实现基于CNN的中文短文本分类

Related tags

Deep LearningTHUCNews-Classification-CNN
Overview

Text Classification with CNN

使用卷积神经网络进行中文文本分类

CNN做句子分类的论文可以参看: Convolutional Neural Networks for Sentence Classification

还可以去读dennybritz大牛的博客：Implementing a CNN for Text Classification in TensorFlow

以及字符级CNN的论文：Character-level Convolutional Networks for Text Classification

本文是基于TensorFlow在中文数据集上的简化实现，使用了字符级CNN对中文文本进行分类，达到了较好的效果。

文中所使用的Conv1D与论文中有些不同，详细参考官方文档：tf.nn.conv1d

环境

  • Python 2/3
  • TensorFlow 1.3以上（我的是2.x）
  • numpy
  • scikit-learn
  • scipy

数据集

使用THUCNews数据集的一个子集进行训练与测试，数据集可在THUCTC：一个高效的中文文本分类工具包下载，请遵循数据提供方的开源协议。

本次训练使用了其中的10个分类，每个分类6500条数据。

类别如下：

体育, 财经, 房产, 家居, 教育, 科技, 时尚, 时政, 游戏, 娱乐

这个子集可以在此下载：链接: https://pan.baidu.com/s/1hugrfRu 密码: qfud

数据集划分如下：

  • 训练集: 5000 x 10
  • 验证集: 500 x 10
  • 测试集: 1000 x 10

从原数据集生成子集的过程请参看helper下的两个脚本。其中，copy_data.sh用于从每个分类拷贝6500个文件，cnews_group.py用于将多个文件整合到一个文件中。执行该文件后，得到三个数据文件：

  • cnews.train.txt: 训练集(50000条)
  • cnews.val.txt: 验证集(5000条)
  • cnews.test.txt: 测试集(10000条)

预处理

data/cnews_loader.py为数据的预处理文件。

  • read_file(): 读取文件数据。
  • build_vocab(): 构建词汇表，使用字符级的表示，这一函数会将词汇表存储下来，避免每一次重复处理。
  • read_vocab(): 读取上一步存储的词汇表，转换为{词：id}表示。
  • read_category(): 将分类目录固定，转换为{类别: id}表示。
  • to_words(): 将一条由id表示的数据重新转换为文字。
  • process_file(): 将数据集从文字转换为固定长度的id序列表示。
  • batch_iter(): 为神经网络的训练准备经过shuffle的批次的数据。

经过数据预处理，数据的格式如下：

Data Shape Data Shape
x_train [50000, 600] y_train [50000, 10]
x_val [5000, 600] y_val [5000, 10]
x_test [10000, 600] y_test [10000, 10]

CNN卷积神经网络

配置项

CNN可配置的参数如下所示，在cnn_model.py中。

class TCNNConfig(object):
    """CNN配置参数"""

    embedding_dim = 64      # 词向量维度
    seq_length = 600        # 序列长度
    num_classes = 10        # 类别数
    num_filters = 128       # 卷积核数目
    kernel_size = 5         # 卷积核尺寸
    vocab_size = 5000       # 词汇表达小

    hidden_dim = 128        # 全连接层神经元数目

    dropout_keep_prob = 0.5 # dropout正则化保留比例
    learning_rate = 1e-3    # 学习率

    batch_size = 64         # 每批训练大小
    num_epochs = 10         # 总迭代轮次

    print_per_batch = 100   # 每多少轮输出一次结果
    save_per_batch = 10     # 每多少轮存入tensorboard

CNN模型

具体参看cnn_model.py的实现。

大致结构如下：

image-20211110151539493

训练与验证

用cmd命令在代码文件所在目录运行 python run_cnn.py train，可以开始训练。

若之前进行过训练，请把tensorboard/textcnn删除，避免TensorBoard多次训练结果重叠。

Configuring CNN model...
Configuring TensorBoard and Saver...
Loading training and validation data...
Time usage: 0:00:14
Training and evaluating...
Epoch: 1
Iter:      0, Train Loss:    2.3, Train Acc:  10.94%, Val Loss:    2.3, Val Acc:   8.92%, Time: 0:00:01 *
Iter:    100, Train Loss:   0.88, Train Acc:  73.44%, Val Loss:    1.2, Val Acc:  68.46%, Time: 0:00:04 *
Iter:    200, Train Loss:   0.38, Train Acc:  92.19%, Val Loss:   0.75, Val Acc:  77.32%, Time: 0:00:07 *
Iter:    300, Train Loss:   0.22, Train Acc:  92.19%, Val Loss:   0.46, Val Acc:  87.08%, Time: 0:00:09 *
Iter:    400, Train Loss:   0.24, Train Acc:  90.62%, Val Loss:    0.4, Val Acc:  88.62%, Time: 0:00:12 *
Iter:    500, Train Loss:   0.16, Train Acc:  96.88%, Val Loss:   0.36, Val Acc:  90.38%, Time: 0:00:15 *
Iter:    600, Train Loss:  0.084, Train Acc:  96.88%, Val Loss:   0.35, Val Acc:  91.36%, Time: 0:00:17 *
Iter:    700, Train Loss:   0.21, Train Acc:  93.75%, Val Loss:   0.26, Val Acc:  92.58%, Time: 0:00:20 *
Epoch: 2
Iter:    800, Train Loss:   0.07, Train Acc:  98.44%, Val Loss:   0.24, Val Acc:  94.12%, Time: 0:00:23 *
Iter:    900, Train Loss:  0.092, Train Acc:  96.88%, Val Loss:   0.27, Val Acc:  92.86%, Time: 0:00:25
Iter:   1000, Train Loss:   0.17, Train Acc:  95.31%, Val Loss:   0.28, Val Acc:  92.82%, Time: 0:00:28
Iter:   1100, Train Loss:    0.2, Train Acc:  93.75%, Val Loss:   0.23, Val Acc:  93.26%, Time: 0:00:31
Iter:   1200, Train Loss:  0.081, Train Acc:  98.44%, Val Loss:   0.25, Val Acc:  92.96%, Time: 0:00:33
Iter:   1300, Train Loss:  0.052, Train Acc: 100.00%, Val Loss:   0.24, Val Acc:  93.58%, Time: 0:00:36
Iter:   1400, Train Loss:    0.1, Train Acc:  95.31%, Val Loss:   0.22, Val Acc:  94.12%, Time: 0:00:39
Iter:   1500, Train Loss:   0.12, Train Acc:  98.44%, Val Loss:   0.23, Val Acc:  93.58%, Time: 0:00:41
Epoch: 3
Iter:   1600, Train Loss:    0.1, Train Acc:  96.88%, Val Loss:   0.26, Val Acc:  92.34%, Time: 0:00:44
Iter:   1700, Train Loss:  0.018, Train Acc: 100.00%, Val Loss:   0.22, Val Acc:  93.46%, Time: 0:00:47
Iter:   1800, Train Loss:  0.036, Train Acc: 100.00%, Val Loss:   0.28, Val Acc:  92.72%, Time: 0:00:50
No optimization for a long time, auto-stopping...

在验证集上的最佳效果为94.12%，且只经过了3轮迭代就已经停止。

准确率和误差如图所示：

accuracy_1

测试

用cmd命令在代码文件所在目录下运行 python run_cnn.py test 在测试集上进行测试。

Configuring CNN model...
Loading test data...
Testing...
Test Loss:   0.14, Test Acc:  96.04%
Precision, Recall and F1-Score...
             precision    recall  f1-score   support

         体育       0.99      0.99      0.99      1000
         财经       0.96      0.99      0.97      1000
         房产       1.00      1.00      1.00      1000
         家居       0.95      0.91      0.93      1000
         教育       0.95      0.89      0.92      1000
         科技       0.94      0.97      0.95      1000
         时尚       0.95      0.97      0.96      1000
         时政       0.94      0.94      0.94      1000
         游戏       0.97      0.96      0.97      1000
         娱乐       0.95      0.98      0.97      1000

avg / total       0.96      0.96      0.96     10000

Confusion Matrix...
[[991   0   0   0   2   1   0   4   1   1]
 [  0 992   0   0   2   1   0   5   0   0]
 [  0   1 996   0   1   1   0   0   0   1]
 [  0  14   0 912   7  15   9  29   3  11]
 [  2   9   0  12 892  22  18  21  10  14]
 [  0   0   0  10   1 968   4   3  12   2]
 [  1   0   0   9   4   4 971   0   2   9]
 [  1  16   0   4  18  12   1 941   1   6]
 [  2   4   1   5   4   5  10   1 962   6]
 [  1   0   1   6   4   3   5   0   1 979]]
Time usage: 0:00:05

在测试集上的准确率达到了96.04%，且各类的precision, recall和f1-score都超过了0.9。

损失函数变化如图所示：

loss

从混淆矩阵也可以看出分类效果非常优秀。

预测

为方便预测，predict.py 展示了一个简单demo的预测。

Owner
Jeremiah
如今的现在早已不是当初的未来、
Jeremiah
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